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This paper introduces a multi-platform LiDAR dataset integrating UAV-borne, terrestrial, and backpack mobile laser scanning for a comprehensive 3D structural representation of a forest plot. The dataset is explicitly designed for calibration, benchmarking, and integration of 3D structural data with ecological observations and allometric models. By providing a benchmark dataset linked to long-term ecological measurements, the work enables testing of registration methods, scanning efficiency evaluation, and linking point clouds with segmentation and biomass estimation.
Forget tedious marker placement: this multi-platform LiDAR dataset achieves accurate forest mapping using marker-free, SLAM-aware protocols, slashing field and processing time.
We present a curated multi-platform LiDAR reference dataset from an instrumented ICOS forest plot, explicitly designed to support calibration, benchmarking, and integration of 3D structural data with ecological observations and standard allometric models. The dataset integrates UAV-borne laser scanning (ULS) to measure canopy coverage, terrestrial laser scanning (TLS) for detailed stem mapping, and backpack mobile laser scanning (MLS) with real-time SLAM for efficient sub-canopy acquisition. We focus on the control plot with the most complete and internally consistent registration, where TLS point clouds (~333 million points) are complemented by ULS and MLS data capturing canopy and understory strata. Marker-free, SLAM-aware protocols were used to reduce field and processing time, while manual and automated methods were combined. Final products are available in LAZ and E57 formats with UTM coordinates, together with registration reports for reproducibility. The dataset provides a benchmark for testing registration methods, evaluating scanning efficiency, and linking point clouds with segmentation, quantitative structure models, and allometric biomass estimation. By situating the acquisitions at a long-term ICOS site, it is explicitly linked to 3D structure with decades of ecological and flux measurements. More broadly, it illustrates how TLS, MLS, and ULS can be combined for repeated inventories and digital twins of forest ecosystems.
